the simulated annealing algorithm introduces random changes in the parameters, but with a virtual “temperature” that gradually decreases. The probability of a chance event is high at the beginning but steadily declines until the system is frozen in an optimal setting.

As we master them, the objects that once seemed attractive lose their appeal, and we redirect our curiosity toward new challenges.

Active engagement takes place in our brains, not our feet. The brain learns efficiently only if it is attentive, focused, and active in generating mental models.

Pure learning, in the absence of any innate constraints, simply does not exist. Any learning algorithm contains, in one way or another, a set of assumptions about the domain to be learned.

anymore: what is known becomes boring.

To learn is to adjust the parameters of an internal model. Learning to aim with one’s finger, for example, consists of setting the offset between vision and action: each aiming error provides useful information that allows one to reduce the gap. In artificial neural networks, although the number of settings is much larger, the logic is the same. Re

My first and most general definition is the following: to learn is to form an internal model of the external world.

the main property of cortical circuits is their plasticity, their ability to adapt to their inputs.

This result directly validates a key prediction of the neuronal recycling hypothesis—the acquisition of a novel skill does not require a radical rewriting of cortical circuits as if they were a blank slate, but merely a repurposing of their existing organization.